Ricerc@Sapienza

Brain tumors are considered to be one of the most lethal types of tumor. Accurate segmentation of brain MRI is an important task for the analysis of neurological diseases. The mortality rate of brain tumors is increasing according to World Health Organization. Detection at early stages of brain tumors can increase the expectation of the patients’ survival. Concerning artificial intelligence approaches for clinical diagnosis of brain tumors, there is an increasing interest in segmentation approaches based on deep learning because of its ability of self-learning over large amounts of data.

Gliomas are considered as the most aggressive and commonly found type among brain tumors. This leads to the shortage of lives of oncological patients. These tumors are mostly by magnetic resonance imaging (MRI) from which the segmentation becomes a big problem because of the large structural and spatial variability. In this study, we propose a 2D-UNET model based on convolutional neural networks (CNN). The model is trained, validated and tested on BRATS 2019 dataset. The average dice coefficient achieved is 0.9694.